What this category of data looks like
Data sources for this category include MES systems, online inspection equipment, laboratory reports, process parameter documents, customer quality standards, and historical defect databases. Data is updated per production batch, collected continuously per shift, or generated as monthly quality reports. Daily documents include control plans, patrol inspection records, SPC reports, finished product inspection reports, nonconforming product review forms, release forms, and others. Fields include batch identifiers, inspection parameters, judgment thresholds, defect descriptions, and review conclusions. Units correspond to physical quantities, percentages, defect levels, and other metrics.
What constraints do these characteristics impose on the document parsing and chunking link
Multi-source heterogeneous data sources create format adaptation requirements. There are both structured batch data from MES systems and unstructured paper or electronic patrol records, SPC reports. High-frequency updated batch-level data requires chunking to match production cycle granularity, to avoid mixing information across batches. Diverse document structures, such as hierarchical control plans and SPC reports with statistical formulas, require parsing logic to adapt to the layout characteristics of different documents. Long-text historical defect databases and quality monthly reports require balancing chunk length and information integrity, to prevent key judgment logic from being split.
How to configure the settings
This configuration rule applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents includes statistical sections of SPC reports and clause groups of control plans. This length preserves the integrity of a single inspection logic and adapts to the field density of batch data |
chunkSplitter | By paragraph + title hierarchy | Documents in this category often have hierarchical structures, such as chapters of control plans and statistical items of SPC reports. Splitting by title hierarchy preserves logical associations and prevents cross-chapter inspection rules from being split |
customPdfParse | Enabled | PDF documents in this category often include embedded tables, such as the inspection item list of finished product inspection reports. Enabling custom parsing preserves table structure and avoids text disorder |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Batch PDF reports in this category, such as monthly quality reports, may include multi-page statistical content. This duration covers the parsing time of large files |
UPLOAD_FILE_MAX_SIZE | 500 MB | Historical defect databases and monthly summary documents in this category may reach large volumes. This upper limit covers conventional batch upload requirements |
chunkSettingMode | Manual configuration | Documents in this category have large format differences. Manual configuration allows adjusting chunking rules for different document types such as control plans and SPC reports, to adapt to business logic |
Three common misconfigurations
- Symptom: PDF parsing interface calls return parsing failure logs, or parsed text has disorder and missing table content. Cause: The
customPdfParseconfiguration is not enabled. The default parsing logic cannot adapt to the embedded inspection tables and statistical formula layouts in this category of PDFs. - Symptom: Pulling document libraries via external interfaces returns 404 status codes, or inspection records in Feishu multidimensional tables cannot be read. Cause: Confusing FastGPT official APIs with internal interfaces. Not using the official document pull interface provided by FastGPT, or not configuring access permissions for the corresponding data source.
- Symptom: After passing multiple inspection report files to the chat interface, only the first file is parsed. Cause: Not correctly passing multi-file parameters according to FastGPT's message format, and not configuring correct type identifiers and content paths for each file.
How to confirm the configuration is complete
- Upload a typical control plan PDF, check if the parsed text retains chapter hierarchies and inspection item lists, to confirm whether
customPdfParseis effective. - Upload a single-page SPC report, check if the chunked text covers the complete statistical section, to confirm whether the value of
chunkSizeadapts to the document length. - Call the FastGPT official document pull interface, pass a preset data source path, and confirm that the returned document content matches the preset data source.
- Upload a 300 MB historical defect database document, confirm that the upload and parsing process does not trigger timeout or size limit error messages.
The values provided are common starting points and should be measured against the reader's own samples.